PyTorch eager
Conv2d Multiply LeakyReLU GELU · width = 256 · height = 256 · batch_size = 64 · in_channels = 64 · fp32 · run 01a03f0e-f6d5…
●passedReported evidence
Not re-observed recently.
Primary measurement
6.08ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 6.82 · min ms 6.03 · std ms 0.0744 · mean ms 6.08
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 256 · height = 256 · batch_size = 64 · in_channels = 64 · fp32
comparison keysha256:632bd10a4537f4ce…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/54_Conv2d_Multiply_LeakyReLU_GELU.py
sha256:8fb0c6a88f3f9b3f976830c…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width256
height256
batch_size64
in_channels64
input_1fp32 [64, 64, 256, 256]
definition comparatornot_asserted
Measurements
latency · max6.82 ms · n=100
latency · mean6.08 ms · n=100
latency · min6.03 ms · n=100
latency · std74.4 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
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Canonical manifest
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{
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"observedAt": "2026-03-05T08:38:15.000Z",
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"metadata": {
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}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Conv2d Multiply LeakyReLU GELU →JSON